The purpose of the Dutch Travel Survey is to produce reliable estimates on mobility of the Dutch population. In this paper mobility trends are estimated at several aggregation levels, using multilevel time series models. The models account for discontinuities induced by two survey redesigns and outliers due to less reliable outcomes in one particular year. The input for the model is direct annual estimates with their standard errors for the period 1999–2017 for a detailed cross-classification in 504 domains. Appropriate transformations for the direct estimates and generalized variance functions to smooth the standard errors of the direct estimates are proposed. The models are fitted in an hierarchical Bayesian framework using MCMC simulations. From the model outputs smooth trend estimates are computed at the most detailed domain level. Predictions at higher aggregation levels obtained by aggregation of the most detailed domain predictions result in a numerically consistent set of trend estimates for all target variables.

中文翻译：

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荷兰小域移动趋势的多级时间序列建模

荷兰旅游调查的目的是对荷兰人口的流动性进行可靠的估计。在本文中，使用多级时间序列模型在多个聚合级别上估计了流动性趋势。这些模型解释了由两次调查重新设计和异常值引起的不连续性，这是由于某一特定年份的结果不太可靠。该模型的输入是 1999-2017 年期间的直接年度估计及其标准误差，用于 504 个领域的详细交叉分类。建议对直接估计和广义方差函数进行适当的变换，以平滑直接估计的标准误差。使用 MCMC 模拟将模型拟合在分层贝叶斯框架中。从模型输出平滑趋势估计是在最详细的域级别计算的。